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https://issues.apache.org/jira/browse/SPARK-17055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15626043#comment-15626043
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Sebastian Souyris commented on SPARK-17055:
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I have been following this discussion because I need to cross-validate
separating the train and test sets by time-stamp. I'm forecasting time series.
[~srowen], how can you select the training and test set using
TrainValidationSplit?
I see that you can setTrainRatio, but not the sets partition explicitly.
from TrainValidationSplit.scala:
val Array(trainingDataset, validationDataset) =
dataset.randomSplit(Array($(trainRatio), 1 - $(trainRatio)), $(seed))
trainingDataset.cache()
validationDataset.cache()
> add groupKFold to CrossValidator
> --------------------------------
>
> Key: SPARK-17055
> URL: https://issues.apache.org/jira/browse/SPARK-17055
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Vincent
> Priority: Minor
>
> Current CrossValidator only supports k-fold, which randomly divides all the
> samples in k groups of samples. But in cases when data is gathered from
> different subjects and we want to avoid over-fitting, we want to hold out
> samples with certain labels from training data and put them into validation
> fold, i.e. we want to ensure that the same label is not in both testing and
> training sets.
> Mainstream packages like Sklearn already supports such cross validation
> method.
> (http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.LabelKFold.html#sklearn.cross_validation.LabelKFold)
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